Dysphagia (swallowing difficulty) is a serious and debilitation condition that often accompanies, stroke, acquired brain injury and neurodegenerative illnesses. Individuals with dysphagia are prone to aspiration, which directly increases the risk of serious respiratory consequences, such as pneumonia. Aspiration can be defined generally as the entry of foreign material into the airway. Such foreign materials may be of many types, for example, such as foods, liquids, vomit, saliva, secretions from the mouth, or other materials.
The measurement of neck vibrations associated with deglutition is known as swallowing accelerometry, a potentially informative adjunct, to bedside screening for dysphagia. Accelerometric measurements are minimally invasive, requiring only the superficial attachment of a sensor anterior to the thyroid notch.
Recent research has forced upon exploiting this vibration signal for dysphagia screening. For example, combining accelerometry and swallowing pressure, Suryanarayanan et al. developed a hand-crafted fuzzy rule-base to classify sixteen patients with dysphagia according to aspiration risk. Additionally, from the physiological perspective, Reddy et al. attributed the accelerometric signal to the extent of laryngeal elevation during swallowing, thus arguing that accelerometry would be of diagnostic value. Furthermore, based on this premise, Das et al. proposed a hybrid fuzzy logic committee on neural networks trained to accurately distinguish between swallows from twelve healthy subjects and sixteen with dysphagia.
Moreover, studies in this area have provided further information. In a paediatric study involving children with dysphagia secondary to cerebral palsy, swallow accelerometry signals were found to be largely nonstationary, while an off-line radial basis classifier using two time-domain features differentiated between manually segmented aspiration events and safe swallows with 80% sensitivity and specificity.
Previous studies have only investigated a small number of swallows and hence the data collected was conducive to manual segmentation by a human analyst. Segmentation algorithms facilitate segmentation of larger collection of data. This is necessary as larger volumes of accelerometry data necessitate an automatic method to mitigate human error due to fatigue or oversight and to ensure consistent segmentation criteria. Such algorithms have been developed in many fields, e.g. heart sounds analysis, electroencephalogram signals analysis, knee joint vibroarthrographic signals analysis and in the analysis of urine magnetomyogran contractions during pregnancy, to name a few. In particular, several successful methods rely on multiple channels of information to enhance segmentation.
Wang and Willett have proposed a very simple algorithm that determines the number of segments automatically. This algorithm is useful but encounters a number of problems when utilized to analyze swallowing accelerometry data. Specifically, the Wang and Willett algorithm is prone to overestimating the number of segments of nonstationary variance, which is an element of swallowing accelerometry signals.
US Patent Applications No. 2005/0283096 presents another example of prior art in the area of study. The patent discloses an apparatus and method for detecting swallowing activity. The method and apparatus disclosed in this patent involve the generation of electrical signals by an accelerometer positioned on the throat of the patient and the receipt and analysis of those signals at a computing device. Gamma distribution is applied to estimate the spread and location parameters within the signals. Through the method and apparatus the type of swallowing activity undertaken may consequently be classified.